Author: Jae, PANews
Computing power has become the "new oil of the 21st century" that powers global AI. The AI-driven computing arms race is transcending the physical boundaries of information technology and deeply penetrating the bloodstream of modern financial infrastructure.
Larry Fink, CEO of global asset management leader BlackRock, once noted that, amid scarce AI ecosystem resources, a futures market tied to computing power may emerge. This prediction was concretely validated in May.
Within just one week, two major players in traditional finance—CME Group and Intercontinental Exchange (ICE), the parent company of the New York Stock Exchange—announced their entry into the GPU computing power futures market.
Computing power is transitioning from an intangible technical resource into a standardized financial asset that can be speculated on, traded, and hedged. The fierce competition among Wall Street giants for pricing control over this new macro commodity marks the official beginning of the era of computing power asset financialization.
GPU futures become Wall Street’s new battleground: ICE dominates globally, CME moves first to secure position
In this race to financialize hash power assets, two Wall Street giants have each chosen distinct entry strategies.
On May 19, ICE, in collaboration with data provider Ornn, entered the market with plans to launch a series of GPU computing power futures contracts based on the Ornn Computing Power Price Index (OCPI).
ICE's OCPI is the world's first computing power index built on actual transaction records. Ornn, through its subsidiary Ornn Data, distributes this index in real time to Bloomberg Terminals to ensure pricing transparency and prevent the issue of "quoted price distortion."
Ornn co-founder and CEO Kush Bavaria believes that computing power has grown into a trillion-dollar market, and ICE's futures listing will provide a risk transfer layer for institutional buyers and computing power operators.
The ICE computing power futures contract covers not only mainstream enterprise-grade high-end GPUs such as the H100, H200, and B200, but also high-end consumer GPUs like the RTX 5090, providing refined hedging options for diverse computing power needs. This means ICE is aiming to seize pricing control across the entire spectrum of computing power—from cloud to edge, and from training to inference.
To further strengthen the industrial foundation of the index, Ornn has partnered with Hyperbolic Labs, one of the world’s largest GPU markets. Its co-founder and CEO, Jasper Zhang, noted that the current GPU market is increasingly resembling a global commodities market, and ICE’s strategy precisely addresses the risk management challenges faced by neoclouds and AI laboratories.
Rather than actively entering the hash rate futures market, ICE is essentially playing catch-up. In fact, CME had already taken the lead a week earlier.
On May 12, CME announced it would collaborate with Silicon Data, a GPU market intelligence and benchmark data provider backed by trading giant DRW, to launch the world’s first hash rate futures contract. As a benchmark in the global derivatives market, CME’s entry signifies that hash rate has officially been incorporated into the category of “macro commodities” recognized by Wall Street.
Unlike ICE’s broad approach, CME’s hash rate futures are anchored to the “H100 Rental Index” compiled by Silicon Data. By daily standardization and tracking of real-time on-demand rental rates across leading cloud providers and emerging GPU cloud platforms, it establishes a unified pricing benchmark for the highly fragmented and opaque spot market.
To avoid depreciation and transportation losses associated with physical delivery, CME’s GPU futures contracts will be cash-settled, with the underlying asset not being physical chips, but rather expectations of future H100 rental prices.
For large-scale cloud service providers, this offers a critical hedging mechanism. When investing billions of dollars in H100 purchases, providers can simply establish a short position in the CME compute futures market to lock in a minimum return on investment (ROI) for their servers, thereby protecting against asset depreciation risks caused by a sharp decline in compute prices.
This approach is very much like the logic that turned crude oil, natural gas, and electricity into commodities back then.
Hash rate futures ignite the battle for pricing power, bringing both opportunities and challenges to financialization.
Since the surge of large models swept the globe, computing power has risen from being merely an "IT resource" to a "strategic asset" fiercely contested by Silicon Valley giants like OpenAI, Anthropic, Google, and Meta. In short, whoever hoards the most GPUs holds the key to entering the AI era.
But problems have arisen: the hash rate market is too expensive and too unpredictable.
Amazon AWS, Microsoft Azure, Oracle, and Google GCP control approximately 78% of global IT power capacity and 69% of H100 supply. Spot rental prices sometimes surge several-fold and other times plummet during chip upgrades. If an AI lab wants to secure computing power a year in advance, it may need to pay a premium of up to double; if it doesn’t lock in, it risks supply shortages.
More problematic is that the hashing power market lacks hedging tools.
DRW founder Don Wilson admitted: The explosive growth of heavy-asset investments like data centers has long been constrained by the lack of effective risk management tools; the launch of a computing power futures market will address this pain point.
Whoever controls the pricing power of computing power controls the Bretton Woods system of the AI era.
The battle for pricing power over computing power between Wall Street’s two giants reveals that this emerging production factor is at a historical crossroads between financialization and commoditization. This evolution is supported by industry cycles but also accompanied by significant potential risks.
From the perspective of supply and demand cycles, the global computing power market is entering a new phase of rebalancing supply and demand. Although earlier explosive growth in AI applications caused severe mismatches in the supply and demand for high-end GPUs, driving rental prices up several-fold, the large-scale completion of data center construction and advancements in chip manufacturing processes will lead to significant price volatility in the spot market, creating an urgent need for forward pricing instruments to mitigate risk.
However, the intangible nature of computing power means it cannot replicate the delivery logic of traditional commodities. Physical chips have a short lifecycle, typically becoming technologically obsolete or depreciating within 18 to 24 months, rendering physical delivery forward contracts invalid due to underlying hardware upgrades. Therefore, the industry has widely adopted the optimal solution: using a “standardized computing power unit,” such as one hour of H100 runtime as the benchmark, combined with cash settlement. Nevertheless, this also increases the complexity of pricing models.
Additionally, the hash rate supply side is highly concentrated, making the spot market essentially an oligopoly. Building a derivatives market on this structure results in an inherent vulnerability in the price discovery mechanism, making futures prices susceptible to indirect manipulation by spot prices.
More importantly, once the hash rate derivatives market fully opens, its leverage characteristics may amplify price volatility in the spot market. The influx of leveraged capital and rising speculative enthusiasm could drive up the cost of acquiring hash rate, turning small and medium-sized AI companies into “victims,” and potentially evolving into a “financial hunt” that further exacerbates the unequal distribution of computing power resources.
Wall Street is still waiting for approval; crypto traders have already moved in.
The two major exchanges on Wall Street are still awaiting regulatory approval, while players in the crypto market have already taken action.
Back in January this year, Architect Financial Technologies, founded by the former president of FTX US, partnered with Ornn to launch a perpetual contract linked to the OCPI-H100 on its AX platform.
As more platforms follow suit, it is possible that CEXs (centralized exchanges) will gradually launch related hash rate futures markets. In addition, they may also introduce structured wealth management products for retail users or dollar-cost averaging products tied to GPU rental rates, further enabling seamless integration between the crypto market and traditional financial macro assets.
Compared to CME and ICE, which are subject to strict regulation and lengthy approval processes, Perp DEXs (decentralized perpetual exchanges) running on smart contracts benefit from greater agility and the institutional advantages of permissionless innovation.
A perp DEX also doesn’t require the lengthy listing processes typical of CEXs—for example, developers only need to stake 500,000 HYPE tokens (with even lower thresholds possible in the future) to list a power perpetual contract pegged to a GPU index on Hyperliquid’s HIP-3 market. This product development capability will enable DeFi to establish a global, borderless, and barrier-free computing power speculation market outside of Wall Street’s regular trading hours.
However, hash rate futures are still an emerging asset class and carry a high risk profile in their early stages. The hash rate market is primarily over-the-counter (OTC), making data sources susceptible to manipulation; in more extreme cases, black swan events such as technological breakthroughs or chip embargoes could cause the hash rate index to experience discontinuous, sharp spikes or crashes. Both scenarios can lead to price distortions, triggering cascading liquidations of highly leveraged contracts.
Regardless, the competition among Wall Street giants for hash rate futures marks a turning point in the integration of AI infrastructure with modern finance.
GPU computing power, previously viewed primarily as an IT resource, is now being transformed into standardized assets that are measurable, tradable, and hedgeable, integrating the allocation logic of technical resources into the global financial system.
As computing power assets become commoditized, their resource allocation logic may shift from relying solely on spot purchases to being more influenced by financial market price signals. In the future, computing power may gradually develop more mature price discovery mechanisms and capital allocation systems, similar to foundational production factors such as energy and electricity.